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Open Access Methodology article

Reranking candidate gene models with cross-species comparison for improved gene prediction

Qian Liu1*, Koby Crammer1, Fernando CN Pereira2 and David S Roos3

Author Affiliations

1 Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA

2 Google, Inc., Mountain View, California, USA

3 Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA

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BMC Bioinformatics 2008, 9:433  doi:10.1186/1471-2105-9-433

Published: 14 October 2008

Abstract

Background

Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features.

Results

We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+.

Conclusion

Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.